This invention relates generally to systems and methods for detecting the occupants of an automobile. More specifically, this invention relates to weight-based and pattern-based automobile occupant detection systems and to methods and systems for making airbag deployment decisions based on information received from an occupant detection system.
In the United States, airbag deployment forces and speeds have been optimized to save 180 lb. males. Unfortunately, the force and speed requirements to save 180 lb. males are tremendous, and present a potentially fatal hazard to young children (especially when seated in rear-facing infant seats) and to small females. There is also a significant danger of physical injury or death from airbag deployment for anyone who is situated too close to the dashboard/steering column or who is otherwise in a vulnerable position during deployment. The National Highway Traffic Safety Administration (NHTSA) has proposed a set of requirements for automobile manufacturers to develop and install xe2x80x9csmartxe2x80x9d airbag systems that will detect an automobile occupant and disable the airbag when the occupant is at risk. It is also desirable to have systems which are able to adjust the deployment force/speed of airbags, as occupant classification becomes more accurate.
Several key terms are used by the NHTSA and most automotive manufacturers for occupant classification. A Rear Facing Infant Seat (RFIS) is a rear facing fixture designed to protect infants up to 30 lbs. An Infant Bed is a flat bed that straps into an automobile seat and allows infant to lie horizontally and sleep. Infant beds and RFISs are designed for infants only (typically up to 30 lbs.). A Forward Facing Child Seat (FFCS) is a forward facing seating fixture designed for children up to 40 lbs. A Booster Seat is also a forward facing seating fixture, but is designed for children heavier than 40 lbs. There are two types of booster seats: namely, booster seats that use the automobile""s safety belt system to protect the child and simply augment the child""s position in the seat and guide the shoulder belt into a proper position; and booster seats that are held by the automobile""s belt system and have folding arms or other methods of restraining the child.
The broad goal of occupant detection/airbag suppression systems is to distinguish between adults, for whom the airbags should deploy, and children, for whom they shouldn""t. Accordingly, occupant classification requirements are based on categories of occupants. Children weighing between 29.5-39.5 lbs. are generally termed xe2x80x9c3-Year Oldsxe2x80x9d (although GM includes children up to 45 lbs.), while those weighing between 46.5-56.5 lbs. are defined as xe2x80x9c6-Year Oldsxe2x80x9d (GM includes children up to 66 lbs.). Female adults weighing between 103-113 lbs. are referred to as xe2x80x9c5th Percentile Femalesxe2x80x9d or xe2x80x9c5th Femalesxe2x80x9d. And adult males weighing approximately 140-150 lbs. are xe2x80x9c50th Percentile Malesxe2x80x9d. It should be noted that despite these generally accepted occupant categories, different automobile manufacturers interpret and endorse variations in the specific occupant detection requirements for their automobiles according to the development status and level of confidence they have in the occupant detection technologies they have evaluated.
FIG. 1 is a graph illustrating the industry deployment and suppression categories for airbag suppression systems. As shown, 5th Percentile Females and 50th Percentile Males are in the deploy section of the graph. In other words, airbags should typically be deployed when occupants within either of those two categories are sensed. The suppression category, on the other hand, encompasses 6-Year Olds, 3-Year Olds, and all types of child seats. When an occupant of any of these types is sensed, airbag deployment should typically be suppressed. A gray zone appears between the suppress and deploy zones. This gray zone represents a zone of uncertainty for occupants who do not clearly register within either the deployment zone or the suppression zone. A gray zone exists because the difference between actual weight/pattern characteristics in an automobile seat for 6-Year Olds and 5th Percentile Females is quite small. In other words, it is very hard to distinguish between 5th Percentile Females and 6-Year Olds based on their in-seat weight or pattern characteristics. Accordingly, there is very little room for error in occupant detection systems.
There are two common approaches to vehicle occupant detection, namely, weight-based detection and proximity detection. As its name implies, the weight-based detection approach uses the weight of the occupant in the seat for occupant classification. As will be discussed below, some conventional weight-based detection systems are strictly weight-based while others employ pattern-based occupant recognition of various types as well.
Because of the obvious physical differences between 5th Percentile Females, 6-Year Olds, and car seats, it might be fairly easy to distinguish between them in some applications. On the surface, therefore, it might appear that distinction between them as vehicle occupants should not be terribly difficult. Unfortunately, however, as the following table, Table 1, illustrates, distinguishing between these occupant categories is a very challenging undertaking.
Table 1 compares typical weights exerted in an automobile seat by an average 5th Percentile Female, 6-Year Old, and 3-Year Old in a forward facing child seat (FFCS). The similarity between these occupant-types in their typical in-seat weights makes it extremely difficult to classify them based solely on this characteristic. As the table indicates, only 2 lbs. separates the average 5th Percentile Female""s typical in-seat weight (75 lbs.) from that of the average 3-Year Old in a FFCS (73 lbs). Only 10 lbs. separates the average 5th Percentile Female""s typical in-seat weight from that of the average 6-Year Old (65 lbs.).
There are several reasons for the similarity of typical in-seat weights between these occupant-types. One reason is that small children will have most of their weight in the seat itself while 5th Percentile Females tend to place more of their leg weight out of the seat. Another reason for the similarity is that the weight of the child seat contributes to the weight measured for a 3-Year Old in a FFCS.
The problem of accurately classifying an occupant based on his or her in-seat weight is compounded as the occupant moves in the seat. This is due to the fact that as the occupant moves in the seat, large variations in in-seat weight might be detected. Simply using the armrest or leaning against the door can reduce the occupant""s weight in the seat. Clothing friction against the seat back and the angle of inclination of the seat back can also affect the amount of weight exerted in the seat. All of these factors make the in-seat weight of an occupant insufficient as a sole source of information for occupant detection.
Accordingly, several variations in the conventional weight-based approach exist. A first variation is based on xe2x80x9cA-surfacexe2x80x9d technologies. A-surface technologies typically use film-type sensors which are placed on the xe2x80x9cAxe2x80x9d surface (i.e., the top surface) of the seat foam, just beneath the trim. A first of these A-surface systems uses the Institution of Electrical Engineers (IEE) Force Sensing Resistor (FSR). The FSR is a force sensor sandwiched between two layers of polyester. The IEE FSR was originally developed by Interlink Electronics, but has been licensed by IEE for automotive use. IEE had the first occupant detection system in production for such companies as Mercedes. Unfortunately, the IEE FSR is notoriously difficult to use and suffers from a high degree of variation in production. Accordingly, these early systems are very simple single sensor designs with the sensor in the center of the seat, designed only to detect the presence or absence of an occupant. The primary reason for this system is to prevent airbag deployment for empty seats to avoid the unnecessary replacement of expensive airbags. There are a number of companies now developing smart airbag systems that use IEE""s FSR technology. A list of these companies can currently be found on the internet.
A second A-surface sensor system uses the Flexpoint bend sensor. One Flexpoint bend sensor is described in detail in U.S. Pat. No. 5,583,476 issued to Gordon Langford in December 1996. The first Flexpoint Bend Sensor was invented by Gordon Langford in the late 1980""s and was used in Mattel""s PowerGlove for the 8-bit Nintendo system. This technology has been in production for the automotive industry""s smart airbag systems only since 1996, however. While the IEE sensor detects force applied to its surface, the Flexpoint bend sensor, when used as an occupant detector, relies on seat foam deflection for force detection. Seat foam typically exhibits a memory, however, and therefore isn""t an ideal mechanical medium for a deflection sensor without its own spring system. To overcome this problem, the Flexpoint system uses Force Concentrating Devices (FCDs) to magnify sensor movement in the foam. Further descriptions of the Flexpoint bend sensor and its applications can currently be found on the internet.
A third A-surface sensor system relies on the Delphi-Delco Bladder for force detection. This system uses a bladder filled with silicone (or a similar substance) and a single pressure transducer. The bladder is mounted in the bottom of the seat pan beneath the seat bottom foam. Unfortunately, this system is only able to approximate weight in the seat and cannot reliably distinguish between 6-Year Olds and 5th Percentile Females. It also cannot accurately classify tightly belted child seats in static (without vehicle motion) situations. This system does, however, sense the motion produced by an object or occupant in the seat to aid in occupant classification. Because tightly belted child seats tend to have less free motion in the seat than live occupants, motion tracking is useful in occupant detection. Accordingly, although not reliable for static situations, these systems can be used to satisfy some of the goals of occupant detection. Presently, Ford is preparing to put this type of system into production.
Each of these various A-surface sensor systems has its own advantages and drawbacks. Each is being used by various companies in an attempt to provide more accurate occupant detection systems. Furthermore, other types of weight-based detection systems continue to be developed.
Seat Frame Systems provide another variation to weight-based occupant detection. Several companies, like TRW Inc. (TRW), have mounted load cells at the base of the seat frame to measure weight in the seat. Unfortunately, these seat frame systems can only estimate occupant weight based on the weight exerted in the seat. Furthermore, the presence of floor-anchored seat belts complicates decisions for these systems. Frame-based systems are, however, fairly good at detecting shifts in weight in the seat during vehicle motion.
Proximity detection offers an alternative to weight-based detection as a way to identify vehicle occupants. Proximity detection is based on the consideration that the danger of injury or death resulting from airbag deployment is directly related to the distance of the occupant from the airbag. In other words, the closer the occupant is positioned to the airbag, the greater the risk of injury from its deployment. One reason why small females are particularly at risk, for example, is because they tend to position their seats closer to the dashboard or steering column containing the airbag. Accordingly, proximity detection systems generally use a combination of ultrasonic and infrared sensors to monitor a region in the airbag deployment path. If an object is in this path, they may elect to disable the airbag. A drawback of proximity detection systems is that they generally have difficulty making the appropriate deployment decision when books, pillows, newspapers, or other objects are held in front of the occupant. Proximity detection systems also frequently have difficulty detecting child seats. Presently, many companies, such as Siemens, TRW, and Bosch, are working on proximity detection systems.
Each of the occupant detection systems described above requires software to translate the information received from the sensors into useable data for occupant classification. Unfortunately, no one has yet been able to provide a hardware/software combination capable of meeting all of the NHTSA""s proposed requirements. None of the known occupant detection systems currently in existence are able to accurately distinguish between 5th Percentile Females and 6-Year Olds.
Accordingly, a need remains for a more accurate way to detect an occupant of a vehicle seat, and to discriminate more precisely among occupants of different size/weight characteristics.
One object of the present invention is to enable a vehicle occupant detection system to classify vehicle occupants accurately.
Another object of the present invention is to enable a vehicle occupant detection system to distinguish accurately between 5th Percentile Females and 6-Year Olds.
Another object of the present invention is to provide an airbag deployment system that makes a suppress or deploy decision based on an accurate identification of a vehicle occupant.
The vehicle occupant classification system of the present invention provides a significant improvement in the art by classifying seat occupants based on sensor data from a calibrated array of sensors using a combination of weight estimation, pattern recognition, and statistical evaluation. The occupant classification results can then be used to make an appropriate airbag deployment state decision. The major modules of this system can include a calibration unit, a weight estimation module, a pattern module, and a decision-making module.
The Calibration Unit receives sensor data from a sensor mat subjected to a calibration force during a calibration process. The purpose of the calibration process is to normalize sensor deflections across the mat to compensate for variations in sensors and for effects of the seat trim and foam on sensor characteristics. Calibration data from the calibration process is stored for use by the other system components.
The Weight Estimation Module uses the calibration data to translate each sensor""s deflection reading (sensor data) due to a seat occupant into a relative deflection value (Sensor DU). The Weight Estimation Module also combines the relative deflection values (Sensor DUs) from all of the sensors to produce a system deflection value (RawDU).
The Pattern Module receives occupant sensor data directly from the sensor mat, and also receives pre-processed data from the Weight Estimation Module. The Pattern Module uses both data inputs to look for traits in the pattern of sensor deflections that are common for objects other than people (also called xe2x80x9clivexe2x80x9d occupants). The Pattern Module can look for edge deflections and other pattern traits to help identify non-live occupants. The Pattern Module can then modify the RawDU from the Weight Estimation Module to produce a final Decision DU based on the pattern traits it identifies. The Decision DU and pattern information from the Pattern Module are sent to the Decision-Making Module for use in the deployment decision-making process.
The Decision-Making Module makes a final airbag deployment state decision by analyzing trends in the deflection values, which are indicative of occupant weight and movement, and in the pattern information. When the deployment status is placed into a deploy state, the airbag will deploy during impact. In a suppress state, however, deployment of the airbag will be prevented. The Decision-Making Module can include various security features to prevent modification of the deployment status if the state-change decision is based on unreliable data.